In today’s distributed computing paradigm, achieving resilience against unpredictable faults is still a challenging task. Several state-of-the-art fault tolerance algorithms are static and rule-based, lacking the ability to foresee future events. This work investigates a novel self-healing framework that employs Long Short-Term Memory (LSTM) networks for fault prediction and a Q-learning-based Reinforcement Learning (RL) methodology for adaptive fault recovery. In this paper, we model the recovery decision process as a Markov Decision Process (MDP) whereby the framework learns the best actions to take autonomously from the system state transitions, as well as any feedback received. A high-fidelity simulation environment was utilized to inject realistic errors so that we could rigorously evaluate the model’s performance. The comparative experimental results demonstrate significant improvements in accuracy (96.3%) and Mean Time to Recovery (MTTR) (34 sec) as well as improved availability (97.2%) compared to baseline and state-of-the-art models. This work highlights the opportunity for hybrid Artificial Intelligence (AI) models to promote resilience in cloud-edge distributed systems. The modular design of this framework also presents learning opportunities for deploying as a lesson plan in courses that focus on both distributed systems and intelligent automation in computer science curricula.

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